Metadata-Version: 2.1
Name: mymlcore
Version: 1.1.2
Summary: A high-performance, extensible machine learning library for Python. fastmlcore provides a rich suite of algorithms, preprocessing tools, metrics, and utilities for rapid prototyping and production-grade ML workflows. Designed for clarity, speed, and flexibility, it empowers researchers, engineers, and data scientists to build, evaluate, and deploy models with ease.
Home-page: https://github.com/arnxv-coder/mymlcore
Author: Arnav Barway AKA arnxv
Author-email: me@arnxv.is-a.dev.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: numpy

# mymlcore

**mymlcore** is a simple, extensible machine learning library for Python.

## Features
- LinearRegression, LogisticRegression, DecisionTree, KMeans, SVM, RandomForest, NaiveBayes, NeuralNetwork
- Preprocessing: StandardScaler, MinMaxScaler, OneHotEncoder, Imputer, PCA
- Metrics: mean_squared_error, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, log_loss, roc_auc_score
- Utilities: train_test_split, cross_val_score, grid_search

## Installation

```bash
pip install mymlcore
```

## Usage Example

```python
from mymlcore import LinearRegression, mean_squared_error
import numpy as np

X = np.array([[2, 600], [3, 900], [6, 1700], [5, 1300], [4, 1050], [7, 1900]])
y = np.array([120000, 180000, 400000, 320000, 250000, 450000])

model = LinearRegression()
model.fit(X, y)
preds = model.predict(X)
print(mean_squared_error(y, preds))
```

## License

MIT
